Ontwikkelingsstoornissen in Centraal-Afrika ontcijferen: klinische en genetische studies

Developmental disorders (DD) are a group of conditions that impact all major domains of development. Individuals with a DD are highly vulnerable due to the high mortality and/or lifelong disability and the high financial and psychological impact. Affected people face many challenges, e.g. a diagnostic odyssey, lack of treatment or direct management and poor knowledge on the natural history. This is in part due to a high etiological heterogeneity, which is genetic in 25-50%. Insight into the genetic cause of a DD is an important entry point to improve the health of the child and his family and helps to combat false beliefs that lead to stigmatization and exclusion. Knowledge and expertise in medical genetics on DD are lacking in most African countries, which stands in sharp contrast to the situation in high-income countries with a tremendous increase in knowledge on the etiology of DD and access to diagnostic facilities as well as guidance of the patient and their family. Exome Sequencing, analyzing the coding and splicing regions of the genome, is recommended as a first-tier test for patients with DD. Unfortunately, data from African populations remains scarce as small percentage of participants in genomics research studies are of non-European ancestry. Although efforts exist to address this gap, progress has been particularly slow in this domain. To date, genetic studies on DD in the Central African population are scarce. We therefore conducted research on phenotyping and genotyping of Congolese individuals with unexplained developmental disorders. The long-term aim of this study is to increase our knowledge of the genetic etiology and phenotypic profile of DD in Central Africa. This will contribute to the etiological diagnosis and management guidance of individuals with a DD. The data obtained in this study will fill the gap of the existing knowledge in the clinical and genetic aspects of DD in Central Africa. For our study, we undertook in-depth phenotypic characterization of 148 Congolese individuals with unexplained developmental disorders recruited at 6 institutions (the University hospital of Kinshasa and 5 specialized schools in two major cities of the country, Kinshasa and Lubumbashi. We introduced a standardized protocol of clinical phenotyping and evaluated the use of Face2Gene for facial dysmorphism assessment. We then performed a family-based exome sequencing on 147 of them to identify the genetic causes. Finally, we studied the genotype-phenotype integration in diagnosed probands by evaluating existing clinical diagnostic criteria and by using Face2Gene for facial recognition syndrome diagnosis. This study was part of DDD-Africa, a unique research project involving two genetic centers in Africa, the Centre for Human Genetics of the University of Kinshasa in the DR Congo and the University of Witwatersrand and the National Health Laboratory (NHLS) in Johannesburg in South-Africa. In the Phenotyping study, we first recruited 148 individuals, mostly as trios, with unexplained DD from 6 institutions and obtained standardized and detailed family and personal history, and physical description for neurodevelopment domains, major and minor anomalies. Probands were predominantly male (a selection bias in our setting), young, pre-school and school-aged. No consanguinity was reported, even though almost half of the probands were from parents originating from the same province. We included mostly individuals with moderate to severe developmental disorders associated with significant behavioral and neurological disorders such as hyperactivity and seizures. Individuals with profound disorders were rarely observed, as neither families nor institutions paid much attention to them. In the majority of individuals, no formal psychometric evaluation was available for the severity of developmental delay or other neurodevelopmental disturbances such as ADHD or autism. Major anomalies were often observed in our cohort, due to a large proportion of probands recruited at the University Hospital where children with complex disorders are followed. We observed a tendance to decreasing incidence of major anomaly with age, consistent with the enrichment of major malformations that are compatible with survival. A broad spectrum of different minor morphological variants was recorded (153 in total) with a median number of 8 per proband. One important aspect of this section is that we found that dysmorphism and the presence of major anomalies were risk factors for severe developmental delay. Since we do not have data on minor anomalies from a local reference population of equivalent ages, consensus gestalt facial evaluation by experienced clinicians remains the golden standard to assess dysmorphism. Second, we evaluated 2D facial photographs of pediatric individuals with a DD from the DR Congo and Belgium as being dysmorphic or not, by D-score analysis compared to consensus gestalt facial evaluation. The D-score in the Congolese showed a good PPV, NPV, and moderate agreement with the gestalt evaluation. There was no significant difference with age and sex, and performance was not different from that in the Belgian cohort. This tool could be used as a supplementary tool to aid in the dysmorphological evaluation of individuals with DD, by identifying those who may benefit from a genetic test. In the Genotyping study, we evaluated the performance of the combined approach (SNV/CNV detection) by performing ES on probands and relatives from 147 families recruited and for whom the DNA passed quality controls. We perform analysis and interpretation of the ES data using an internal manual filter designed for this study and using state-of-art open-access interpretation and analysis platforms including Seqr, ClinViewer, and Franklin. Ninety-eight percent of individuals recruited were successfully sequenced illustrating the excellent quality of the DNA extracted in our laboratory. The combined approach SNV-CNV analysis has enabled us to achieve a diagnostic yield of 43% (63/147, including 43 probands with SNVs, 19 with CNVs and one with SNV+CNV). No additional variant detection methods were yet applied to address the shortcomings of ES in detecting UPDs, mosaic variants, large de novo indels, and mobile element insertions. This suggests that our yield would be even higher if we applied them. Significantly more females were solved than males (58% vs 35.1%, p=0.008), and the majority of solved cases involved trios (46.8%) compared to duos (37.3%). As assumed in the first study, depending on the families recruited (sporadic vs. familial), de novo variants (49.3%) accounted for the majority of the inheritance pattern observed in our cohort, confirming previous reports that de novo variants are enriched in neurodevelopmental disorders. Most of SNVs (37/47) resulted in an AD disorder. Based on the new classification of X-linked inheritance, 2 resulted in group 1, one in group 2b and two in group 3. With regard to the CNVs, the 3 CNV calling tools identified 22 P/LP (15 deletion and 7 duplication) CNVs after aCGH and ROH confirmations analysis. Most deletions detected were larger than 1Mb. A total of 57 different disorders were identified (39 SNVs and 18 CNVs) among the 63 probands. Six disorders were observed in more than one proband (SETBP1-haploinsufficiency Disorder, Noonan syndrome, Phelan-McDermid syndrome, SYNGAP1-related intellectual disability, Wolf-Hirschhorn Syndrome and Williams-Beuren Syndrome). Two probands had a dual diagnosis, and two others were supposed to have a dual diagnosis. This study demonstrates that this combined SNV/CNV approach can be routinely incorporated into the standard clinical diagnostic workflow for patients with unresolved DD in LMICs. In the Genotype-phenotype integration study, we evaluated the use of existing validated clinical scoring systems in the literature as well as Face2Gene for automatic facial syndrome recognition in the 63 individuals diagnosed (with 57 different diagnoses, 39 SNVs and 18 CNVs). For probands with dual diagnosis, we analyzed the clinical features to determine how well they matched the individual syndromes and identified discriminating phenotypes between the two diagnoses. Eleven validated clinical scoring systems were available (KBG, Noonan, CHARGE, Stickler type I, Rett, Kabuki, Pitt-Hopkins, Wolf-Hirschhorn, Prader-Willi, Angelman and Williams syndromes). All probands reached the threshold score needed to establish the diagnosis, except for the proband with Stickler I syndrome who scored 4 points on 5, but was consistent with his young age and the absence of some imaging investigations. Face2Gene matched the correct disorder for 23 of 35 molecular confirmed conditions (65.7%) for which a mask was available and 14/23 ranked first. This indicates that it can be used as an alternative for differential diagnosis. We found a large overlapping phenotype for one of the two probands with dual diagnosis whereas for the other proband, we observed a very distinctive phenotype between the 2 disorders. As for many of the disorders, clinical criteria are lacking, and objective and quantitative matching of reported phenotype in the literature for the disease is limited. Nevertheless, the genotype-phenotype integration performed in this study was consistent with the majority of dysmorphic probands receiving a diagnosis, regarding the existing clinical diagnostic criteria and the AI-based genetic syndrome recognition, demonstrating that it can be applied in genomic analyses. In conclusion, this study, which consisted of in-depth phenotypic and genotypic analysis, enabled us to standardize our phenotypic approach, achieved a diagnostic yield as high as those reported in other large-scale studies. These data provide a solid reference basis for further studies in Africa.